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Discussion papers
https://doi.org/10.5194/nhess-2019-125
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-125
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 Jul 2019

Submitted as: research article | 08 Jul 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Dynamic path dependent landslide susceptibility modelling

Jalal Samia1,2, Arnaud Temme3,4, Arnold Bregt1, Jakob Wallinga2, Fausto Guzzetti5, and Francesca Ardizzone5 Jalal Samia et al.
  • 1Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, 6708 PB Wageningen, Droevendaalsesteeg 3, The Netherlands
  • 2Soil Geography and Landscape group, Wageningen University & Research, 6708 PB, Wageningen, Droevendaalsesteeg 3, The Netherlands
  • 3Department of Geography, Kansas State University, 920 N17th Street, Manhattan, KS, 66506, United States
  • 4Institute of Arctic and Alpine Research, University of Colorado, Campus Box 450, Boulder, CO 803309-0450, Colorado, United States
  • 5Istituto di Ricerca per la Protezione Idrogeologica, Consiglio Nazionale delle Ricerche, via Madonna Alta 126, 06128 Perugia, Italy

Abstract. This contribution tests the added value of including landslide path dependency in statistically-based landslide susceptibility modelling. A conventional pixel-based landslide susceptibility model was compared with a model that includes landslide path dependency, and with a purely path dependent landslide susceptibility model. To quantify path dependency among landslides, we used a Space-Time Clustering (STC) measure derived from Ripley's space-time K function implemented on a point-based multi-temporal landslide inventory from the Collazzone study area in central Italy. We found that the values of STC obey an exponential decay curve with characteristic time scale of 17 years, and characteristic space scale of 60 meters. This exponential space-time decay of the effect of a previous landslide on landslide susceptibility was used as the landslide path dependency component of susceptibility models. We found that the performance of the conventional landslide susceptibility model improved considerably when adding the effect of landslide path dependency. In fact, even the purely path dependent landslide susceptibility model turned out to perform better than the conventional landslide susceptibility model. The conventional plus path dependent and path dependent landslide susceptibility model and their resulted maps are dynamic and change over time unlike conventional landslide susceptibility maps.

Jalal Samia et al.
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Short summary
For Collazzone study area in Italy, we quantified how much landslides follow others using Ripley's K, finding that susceptibility is increased within 60 m and 17 years from a previous landslide. We then calculated the increased susceptibility for every pixel and for the 17-timeslice landslide inventory. We used these as additional explanatory variables in susceptibility modelling. Model performance increased substantially with this landslide history component included.
For Collazzone study area in Italy, we quantified how much landslides follow others using...
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